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NORTH AMERICAN Newsline JUNE 12, 2026 | The Indian Eye 34

AI Meets Metabolic Medicine:

How Cloud Systems Are Personalizing

Weight Loss Drug Therapy

Moving past one-size-fits-all treatments with cloud systems that predict and optimize patient response.

Shivam Gupta is a Software Development Engi-
neer at AWS, specializing in distributed systems,
cloud infrastructure, backend engineering, and ap-
plied AI. He focuses on building reliable, scalable,
and observable large-scale production systems.
He holds a Master’s in Computer Science
from the University of Florida, where he researched
machine learning and generative AI using HiPer-
Gator supercomputing infrastructure. Previously,
he built cloud-native backend systems and finan-
cial data platforms at Barclays. Shivam is also an
active researcher and keynote speaker at interna-
tional conferences covering AI, cybersecurity, and BY SHIVAM GUPTA AND SHIVAI fully adjusted. Shivai Gupta an expert in digital bio-
distributed systems. technology, highlights the importance of using bio-
eight loss drugs like GLP-1 medications medical data and machine learning together to better
have changed how doctors treat obesity, understand how the body reacts to these medications.
Wbut they come with a major challenge.
Not everyone responds to them in the same way. However, the goal is not to replace doctors.
Some people lose significant weight quickly, while
others see slower or limited results. The question Instead, it is to give them better tools. AI
now is not just whether these drugs work, but who can highlight patterns that are difficult to
they work best for and why.
This is where artificial intelligence and cloud see manually, such as early signs that a
computing are starting to play an important role. In- drug is not working well or that a patient
stead of looking at a patient in isolation, AI systems
can analyse many signals at once, such as changes in may experience side effects.
Shivai is a biotechnology professional with a PhD body weight over time, eating habits, activity levels
in Biomedical Sciences (Immunology), specializ- from wearable devices, medical history, and even There are still challenges. Health data is often
ing in life sciences, data engineering, and AI-driv- how consistently someone takes their medication. By incomplete, inconsistent, and sensitive. Protecting
en R&D transformation. Currently a Scientific & combining all of this information, AI can help predict patient privacy and ensuring fairness across different
Digital Solutions Consultant at Genedata (Dana- how a person might respond to a specific treatment. populations is critical. There is also the risk of over-re-
her), she helps global pharma and biotech compa- Cloud computing makes this possible at scale. lying on data trends without understanding individual
nies optimize enterprise platforms and data pipe- Health data is generated in many places, hospitals, human differences.
lines for biologics and bioprocess R&D using SQL pharmacies, mobile apps, and fitness devices. The Even with these challenges, the direction is
and Python. Previously, at the University of Flori- cloud brings all of this information together securely clear. Healthcare is slowly shifting from a “one-size-
da, she led machine learning and in-silico screen- so it can be analysed in real time. This allows doctors fits-all” approach to a more personalized system,
ing research for autoimmune disease therapies, and health systems to monitor progress continuously where treatments are continuously refined based on
contributing to multiple first-author publications. rather than relying on occasional checkups. real-world data.
She bridges scientific depth with digital fluen- Shivam Gupta a software developer and expert in AI and cloud computing are becoming the foun-
cy to build scalable, analytics-driven frameworks ML, explains this shift as moving toward predictive dation of this change, helping make weight loss
for biopharma innovation. healthcare systems, where cloud-based AI helps an- treatment more precise, adaptive, and effective for
ticipate how patients will respond before treatment is more people.

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